Local Feature Hashing with Graph Regularized Binary Auto-encoder for Face Recognition

In recent years, learning-based hashing has gained increasing research interest in face recognition due to its encouraging storage and computational efficiency. However, learning high-quality binary codes that yield appealing recognition performance is still a challenge. In addition, since the neighborhood structure is important for discrimination, it should be taken into account to capture useful nearest neighbors. In this paper, we propose a novel and efficient graph-based hashing model, called local feature hashing with graph regularized binary auto-encoder (LFH-GRBA), to learn feature representations for face recognition. It considers the semantic neighborhood of the face data and seeks to reconstruct a face image from the learned binary codes. Specifically, the graph Laplacian is incorporated into the binary auto-encoder as a regularizer to exploit the semantical neighborhood information of the face data. To make such a model computational efficiency, a tractable alternating optimization approach is proposed to solve the objective function, yielding high-quality binary codes to well capture the neighborhood structure and provide high discrimination. Moreover, the discrete cyclic coordinate descent (DCC) method is adopted to directly learn binary codes in the Hamming space to eliminate the accumulated quantization errors. Extensive experimental results on three widely used datasets including FERET, LFW and PaSC datasets demonstrate that the proposed LFH-GRBA model outperforms most state-of-the-art face representation methods.